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Statistical quantitative methods in ...

Ahlawat, Samit.

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Statistical quantitative methods in finance = from theory to quantitative portfolio management /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical quantitative methods in finance/ by Samit Ahlawat.
Reminder of title:	from theory to quantitative portfolio management /
Author:	Ahlawat, Samit.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xvi, 295 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Model -- Chapter 3: Kernel Regression -- Chapter 4: Regime Switching Models -- Chapter 5: Bayesian Methods -- Chapter 6: Tobit Regression -- Chapter : Random Forest -- Chapter 8: Generalized Method of Moments -- Chapter 9: Benchmarking Machine Learning Models.
Contained By:	Springer Nature eBook
Subject:	Finance - Statistics. -
Online resource:	https://doi.org/10.1007/979-8-8688-0962-0
ISBN:	9798868809620

Statistical quantitative methods in finance = from theory to quantitative portfolio management /
Ahlawat, Samit.

Statistical quantitative methods in financefrom theory to quantitative portfolio management /[electronic resource] :by Samit Ahlawat. - Berkeley, CA :Apress :2025. - xvi, 295 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Model -- Chapter 3: Kernel Regression -- Chapter 4: Regime Switching Models -- Chapter 5: Bayesian Methods -- Chapter 6: Tobit Regression -- Chapter : Random Forest -- Chapter 8: Generalized Method of Moments -- Chapter 9: Benchmarking Machine Learning Models.

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications.

ISBN: 9798868809620

Standard No.: 10.1007/979-8-8688-0962-0doiSubjects--Topical Terms:

677192
Finance
--Statistics.

LC Class. No.: HG176.5

Dewey Class. No.: 332.015195

Statistical quantitative methods in finance = from theory to quantitative portfolio management /
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520 $a Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications.
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